CN102005760A - Universal wind power short-term forecasting method - Google Patents

Abstract

The invention relates to a universal wind power short-term forecasting method, which comprises the following steps of: (1) collecting the global large scale meteorological forecast field data and terrain, vegetation and seawater temperature information; (2) creating a WRF (The Weather Research and Forecasting) mode; (3) inputting information collected in the step (1) to the WRF mode in the step (2) to acquire wind speed and wind direction forecast data in the range of wind power field; (4) correcting the wind speed; and (5) solving the forecast wind power by using a blower power curve in the step (4). In the universal wind power short-term forecasting method of the invention, the forecast wind speed can be obtained by collecting the global large scale meteorological forecast field data and terrain, vegetation and seawater temperature information and inputting the information collected to the WRF mode, and the corrected forecast wind speed value is more close to actually measured wind speed via wind speed correction, therefore, the short-term wind power can be precast more accurately through the corrected forecast wind speed value. The method is free from historical operation data of the wind power field and the forecast scheme is flexible with less limiting conditions.

Description

A kind of general wind power short-period forecast method

[technical field]

The present invention relates to the wind generator system field, relate in particular to method for forecasting short-term wind-electricity power, specifically utilize the high-resolution numerical model to carry out the method for wind energy turbine set wind power forecast in conjunction with the power of fan curve.

[background technology]

In recent years; the continuous aggravation of energy crisis and environmental problem; caused showing great attention to of the international community and the public; in order to overcome the restriction of energy supply and demand contradiction; promote economic sustainable development; improve the human settlement, greatly develop the focus that the regenerative resource that comprises wind energy has become national governments and scientific and technological circle's extensive concern.Under these circumstances, the wind power generation cause of China has presented good growth momentum, large-scalely is incorporated into the power networks wind power generation development rapidly, country in succession in Jiuquan, Xinjiang, Jiangsu, Meng Dong, Meng Xi, Jilin, Hebei planned seven ten million multikilowatt wind-powered electricity generation bases.Wherein construction plan has been finished in ten million multikilowatt wind-powered electricity generation base, Jiuquan, and the part wind energy turbine set is generated electricity by way of merging two or more grid systems.

The characteristics of electric power system maximum are real-time dynamic equilibrium, just will guarantee just balance of electricity that each is sent constantly and the electricity that is consumed, and could guarantee the stable and safety of electric power system.Before wind power generation inserted electric power system, what electric power system was faced was foreseeable load and the power supply that can control, on the basis of load prediction, guarantees the real-time dynamic equilibrium of electric power system by the scheduling controlling to generating.

When wind-powered electricity generation is linked into electric power system as a kind of power supply, its intermittence and fluctuation, increased the peak regulation difficulty of electric power system, if to wind-powered electricity generation do not do in advance, survey and dispatching management, just require in the middle of electric power system, to leave the reserve capacity that equates with the wind-powered electricity generation capacity, the fluctuation of balance wind-powered electricity generation.Along with the increase of wind-powered electricity generation ratio in power supply, it is big that the peak-valley difference of electric power system further becomes, and this just need carry out peak regulation by a larger margin, if still adopt electric power system to stay the standby mode of whole wind-powered electricity generation capacity, electric power system can't normally move.

And simple increase spinning reserve capacity has a series of shortcoming, and at first fired power generating unit average load rate, average output descend, and the economy of the power system operation that makes reduces; Secondly the above unit of China 80% is a fired power generating unit, utilizes thermoelectricity balance wind-powered electricity generation, and the words of carrying out peak regulation can make coal consumption increase, and greenhouse gas emissions increase; Its minimum peak regulation limit of fired power generating unit is roughly at 50%-60% in addition, if along with the peak-valley difference of the increase electric power system of wind-powered electricity generation installation ratio greater than the minimum peak regulation limit of system the time, wind-powered electricity generation will meet with and ration the power supply, and can not be linked into electric power system.

In order to guarantee that wind-powered electricity generation inserts on a large scale, the best way is exactly to improve power supply architecture, the quantity that increases peaking power source such as water power, pumped storage, pneumoelectric etc., next is exactly to build the high-power energy storage device that fills soon, puts soon, but no matter be to change the power supply architecture or the exploitation of energy storage device, also be difficult at present accomplish in one move, had experience to show to build forecast comparatively accurately, wind power forecast system that function is comparatively complete, be to solve one of key measure that wind-powered electricity generation is incorporated into the power networks on a large scale.

Power Output for Wind Power Field is predicted, wind power is included in the operation plan of electrical network, can make power scheduling department according to predicting the outcome, electrical production and scheduling are made more reasonable, effective plan, for the generation schedule of scheduling schedule system, guarantee electric power system safe and stable operation, reduce reserve capacity and operating cost and electricity market effectively managed etc. all significant, be to solve large-scale wind power to insert one of effective measures of dispatching of power netwoks problem behind the electrical network.

Erik L., Frank, scholars such as Bailey are at document 1. " Wind Power Meteorology.Part II:Siting and Models. " (Wind Energy.1998,1:55-72.) 2. " Modellingthe Wind Climate of Ireland. " (Boundary-Layer Meteorology, 1997,85:359-378.) 3. " Short-Term Wind Forecasting. " (Proceedings of the European Wind Energy Conference, Nice, Frace, 1-5March 1999, PP.1062-1065, ISBN1 902916 X.) pointing out in that the integrated system that adopts numerical forecast pattern and wind power statistical forecast model to combine forecasts, is the effective ways of wind energy turbine set wind power short-period forecast.Its juche idea is to utilize numerical value sky forecast that the forecast informations such as wind speed, wind direction of axial fan hub height are provided, and utilizes the forecast data of wind speed and direction and the wind energy turbine set wind power record material of the same period to set up wind power forecast statistics model then and carries out the wind power forecast.

External wind power prediction research work starting is morning, and more representational method mainly contains: Denmark

The Ewind of the Prediktor forecast system of National Laboratory, Hispanic LocalPred forecast system and the U.S. etc.The Prediktor forecast system at first utilizes numerical weather prediction model HIRLAM that the wind speed profile of wind energy turbine set region is provided, and utilizes WA then ^sP further takes all factors into consideration factors such as near the barrier of wind energy turbine set, roughness variation provides resolution higher wind speed forecast, at last by the energy output computing module

Park calculates the wind energy turbine set wind power on the wind speed basis of forecast.The LocalPred forecast system at first utilizes high-resolution mesoscale model MM5 or NWP pattern in conjunction with weather forecast fields such as fluid mechanics computed in software wind speed, by statistical module (MOS) the forecast wind speed is corrected again, gone out force data by history at last and carry out power with power output model that the same period, meteorological field such as wind speed was set up and forecast.The Previento forecast system is corrected wind speed in conjunction with the influence of wind energy turbine set surrounding terrain, roughness of ground surface and thermal stratification on the basis that utilizes numerical model forecast axial fan hub place height wind speed, carries out the power forecast by power forecast module at last.

The existing short-term wind-electricity power forecast system major part of China also adopts this numerical model to combine with statistical method, utilize wind energy turbine set history exert oneself with the same period numerical model forecast that the mode that the result carries out the modeling of wind power forecasting model develops.But establish by cable in Chinese feature and to send out the area larger, the electrical network rack is comparatively weak, the wind energy turbine set phenomenon of rationing the power supply is comparatively serious, the wind energy turbine set historical data is well not representative, in addition, 7 ten million multikilowatt wind-powered electricity generation bases such as the Jiuquan that country starts in succession, Hami are newly-built wind energy turbine set, do not have the historical wind-powered electricity generation record material of exerting oneself, and the method that above-mentioned in this case numerical model combines with statistical method is suitable.

[summary of the invention]

The purpose of this invention is to provide a kind of general wind power short-period forecast method, this forecasting procedure can forecast that effectively the wind-powered electricity generation in its 48 hours exerts oneself at put into operation wind energy turbine set and newly-built wind energy turbine set, and formulating for dispatching of power netwoks and generation schedule provides rational data support.

To achieve these goals, the present invention adopts following technical scheme:

A kind of general wind power short-period forecast method may further comprise the steps:

(1) gathers global large scale weather forecast field data and provide initial field and 7 days lateral boundary conditions by a definite date for the WRF pattern; Collection landform, vegetation, extra large temperature data provide underlying surface information for the WRF pattern;

(2) set up WRF mode computation meteorological field information; Described WRF pattern utilizes following equation group to carry out the calculating of wind speed, temperature, air pressure forecast fields;

The Eulerian equation of flux form:

${\∂}_{t}U+(\▿\·V_u)-{\∂}_x\left(p{\∂}_{\mathrm{\η}}\mathrm{\φ}\right)+{\∂}_{\mathrm{\η}}\left({p\∂}_x\mathrm{\φ}\right)=F_U$

${\∂}_{t}V+(\▿\·\mathrm{Vv})-{\∂}_y\left(p{\∂}_{\mathrm{\η}}\mathrm{\φ}\right)+{\∂}_{\mathrm{\η}}\left(p{\∂}_y\mathrm{\φ}\right)=F_V$

${\∂}_{t}W+(\▿\·\mathrm{Vw})-g({\∂}_{\mathrm{\η}}p-\mathrm{\μ})=F_w$

${\∂}_t\mathrm{\Θ}+(\▿\·\mathrm{V\θ})=F_{\mathrm{\Θ}}$

${\∂}_t\mathrm{\μ}+(\▿\·V)=0$

${\∂}_t\mathrm{\φ}+{\mathrm{\μ}}^{-1}[(V\·\▿\mathrm{\φ})-\mathrm{gW}]=0$

The density equation:

${\∂}_{\mathrm{\η}}\mathrm{\φ}=-\mathrm{\α\μ}$

Wherein, η=(p _h-p _Ht)/μ, μ=(p _Hs-p _Ht), p _hBe the air pressure of gas place layer, p _HsBe surface pressure, p _HtBe mode layer top air pressure; V=μ v=(U, V, W),

Θ=μ θ, (u, v w), are the component of speed air quantity in two horizontal directions and a vertical direction to v=, and θ is a megadyne temperature, and φ=gz, g are acceleration of gravity, and α=1/ ρ, ρ are atmospheric density;

(3) large scale weather forecast field data that step (1) is gathered and the WRF pattern in the underlying surface information input step (2) obtain wind speed, wind direction, temperature, the air pressure forecast data in the wind energy turbine set scope;

(4) wind speed is corrected;

By near the anemometer tower the wind energy turbine set survey wind data and the same period wind energy turbine set the power trace data utilize multiple regression procedure to set up wind speed to correct model, step (3) is obtained forecasting that wind speed is corrected obtains the forecast amendment wind speed;

(5) above-mentioned steps (4) is obtained the forecast amendment wind speed and utilize the power of fan curve to obtain each blower fan to exert oneself, adding up obtains the wind energy turbine set gross capability.

The equation of correcting model in the step (4) is:

y＝90.8071+0.610167×a-0.0360962×b-0.102678×c

Wherein y represents to correct the back wind speed, and unit is: m/s; A represents to forecast wind speed, and unit is: m/s; B represents to forecast temperature, and unit is: degree centigrade; C represents to forecast air pressure, and unit is: hPa.

Compared with prior art, the present invention has the following advantages: a kind of general wind power short-period forecast method of the present invention, by gathering global large scale weather forecast field data and landform, vegetation, extra large temperature data, input WRF pattern just can obtain forecasting wind speed, correcting the forecast amendment air speed value that just can obtain more through wind speed, by this forecast amendment air speed value accurate forecast short-term wind-electricity power more near true wind speed; The inventive method does not need the wind energy turbine set history data, and the forecast scheme is flexible, restrictive condition is less; Only need near anemometer tower data of wind energy turbine set and wind electric field blower type information, be applicable to all kinds of the operation or newly-built wind energy turbine set.

[description of drawings]

Fig. 1 is a WRF pattern vertical coordinate schematic diagram;

Fig. 2 is a wind power forecast system schematic diagram;

Fig. 3 is the different month forecast in anemometer tower place wind speed and observation wind speed mean error figure;

Fig. 4 is that 70 meters height of certain anemometer tower on March 9th, 2010 are observed wind speed and the comparison diagram that forecasts wind speed;

Fig. 5 is that 70 meters height of certain anemometer tower on April 14th, 2010 are observed wind speed and the comparison diagram that forecasts wind speed;

Fig. 6 is the record wind power and forecast wind power comparison diagram of certain typhoon machine on March 9th, 2010;

[embodiment]

Below in conjunction with accompanying drawing the present invention is done and to describe in further detail.

With reference to Fig. 2, forecasting procedure of the present invention comprises meteorological with the wind power forecast system and underlying surface data module, WRF mode computation module, wind speed are corrected module, power computation module, user's display interface.

Weather station data, large scale forecast fields data are converted into the needed data format of WRF through the pre-treatment process, for the calculating of WRF pattern provides initial gas image field information; Landform, vegetation, extra large temperature data provide underlying surface information for the WRF pattern; Utilize multiple regression procedure that wind energy turbine set forecast wind speed is corrected; Utilize the power of fan curve will correct wind speed and be converted into wind-powered electricity generation and exert oneself, at last the result is shown to user terminal.

The WRF pattern of carrying out weather forecast calculating will be under the parallel high-performance computer environment, this computer environment comprises 12 computing nodes, each node contains 4 CPU, each CPU has two nuclears, can realize mutual communication, file-sharing and concurrent operation between the computing node, and be furnished with certain data space.

WRF mode computation flow chart mainly comprises three parts: (1) prepares meteorological data, prepares the meteorological field data on required forecast date for mode computation; (2) formulate pattern framework, according to wind energy turbine set position and the central point of area deterministic model, the nested number of plies, every layer of nested area that surrounds etc.; (3) the WRF pattern utilizes above-mentioned information to carry out the calculating of weather forecast field.

Forecast example below in conjunction with certain wind energy turbine set is elaborated, and this wind energy turbine set area is about 30 square kilometres, and the forecast date is on March 9th, 2009, and the forecast timeliness is 24 hours.

(1) prepares meteorological data

The WRF pattern needs the meteorological field data as its initial condition and lateral boundary conditions, for pattern provides initial gas image field information and boundary information, and the result of calculation of restriction mode itself.

The meteorological field data adopts NCEP lattice point data, horizontal resolution is 1 ° * 1 °, vertical direction comprises 1000hPa, 975hPa, 950hPa, 925hPa, 850hPa, 800hPa, 750hPa, 700hPa, 650hPa, 600hPa, 550hPa, 500hPa, 450hPa, 400hPa, 350hPa 300hPa, 250hPa, 200hPa, 150hPa, 100hPa, 70hPa, 50hPa, 30hPa, 10hPa, 24 barospheres.By the pre-treatment process WPS of pattern, data and the required forms of the pattern that is converted into such as the wind speed of lattice point, temperature, pressure, humidity, geopotential unit in the extraction NCEP data.

In addition, need to prepare wind energy turbine set in-scope interior landform, the vegetation data in corresponding month and water surface temperature data.

(2) formulate pattern framework

This wind energy turbine set area is 30 square kilometres, and innermost layer nested region area should be greater than 30 square kilometres, so that wind energy turbine set is all included.Stablize needs and pattern restriction operation time according to mode computation, determine that the innermost layer horizontal resolution is 1km, the horizontal direction lattice point number is 37 * 37.Because NCEP forecast fields resolution is 110km, need to fall yardstick the 110km resolution data is reduced to innermost layer 1km resolution data through power, therefore adopt 4 to repoint the cover zone, each layer resolution is respectively 27km, 9km, 3km, 1km.

(3) WRF mode computation

The WRF pattern utilizes following equation group to carry out the calculating of forecast fieldses such as wind speed, temperature, air pressure.The Eulerian equation of flux form:

${\∂}_{t}U+(\▿\·V_u)-{\∂}_x\left(p{\∂}_{\mathrm{\η}}\mathrm{\φ}\right)+{\∂}_{\mathrm{\η}}\left({p\∂}_x\mathrm{\φ}\right)=F_U$

${\∂}_{t}V+(\▿\·\mathrm{Vv})-{\∂}_y\left(p{\∂}_{\mathrm{\η}}\mathrm{\φ}\right)+{\∂}_{\mathrm{\η}}\left(p{\∂}_y\mathrm{\φ}\right)=F_V$

${\∂}_{t}W+(\▿\·\mathrm{Vw})-g({\∂}_{\mathrm{\η}}p-\mathrm{\μ})=F_w$

${\∂}_t\mathrm{\Θ}+(\▿\·\mathrm{V\θ})=F_{\mathrm{\Θ}}$

${\∂}_t\mathrm{\μ}+(\▿\·V)=0$

${\∂}_t\mathrm{\φ}+{\mathrm{\μ}}^{-1}[(V\·\▿\mathrm{\φ})-\mathrm{gW}]=0$

The density equation:

${\∂}_{\mathrm{\η}}\mathrm{\φ}=-\mathrm{\α\μ}$

Wherein, η=(p _h-p _Ht)/μ, μ=(p _Hs-p _Ht), p _hBe the air pressure of gas place layer, p _HsBe surface pressure, p _HtBe mode layer top air pressure, be illustrated in figure 1 as shown in the WRF pattern coordinate schematic diagram.V=μ v=(U, V, W),

Θ=μ θ, (u, v w), are the component of speed air quantity in two horizontal directions and a vertical direction to v=, and θ is a megadyne temperature, and φ=gz, g are acceleration of gravity, and α=1/ ρ, ρ are atmospheric density;

Calculate the data that generate through above-mentioned equation group, utilize the post-processing module of WRF pattern at last, export needed forecast wind speed, wind direction, temperature, barometric information according to blower fan position and blower fan height;

(4) wind speed is corrected

By near the anemometer tower the wind energy turbine set survey wind data and the same period wind energy turbine set the power trace data utilize multiple regression procedure to set up wind speed to correct model, WRF model predictions wind speed is further corrected, obtain the forecast result on the same day;

Fig. 3 exists similar rule as can be seen for forecast wind speed and anemometer tower place wind speed observation air speed error figure between each month, so wind speed and temperature, air pressure are closely related to forecasting wind speed, temperature, air pressure and observing wind speed set up multivariate regression models again;

The equation of correcting model is:

y＝90.8071+0.610167×a-0.0360962×b-0.102678×c

Wherein y represents to correct the back wind speed, and unit is: m/s; A represents to forecast wind speed, and unit is: m/s; B represents to forecast temperature, and unit is: degree centigrade; C represents to forecast air pressure, and unit is: hPa.

Be respectively the comparison diagram of forecast wind speed after 70 meters height were observed wind speed and corrected on March 9th, 2010 and certain anemometer tower on April 14th, 2010 as shown in Figure 4 and Figure 5, correct the very approaching observation wind speed of back wind speed as can be seen, forecast precision is high to be that accurately the forecast wind power is laid a solid foundation.

(5) wind power calculates

Step (4) is corrected wind speed utilize the power of fan curve to obtain each blower fan to exert oneself, adding up obtains the wind energy turbine set gross capability.As Fig. 6 is certain wind energy turbine set power forecast result on March 9th, 2010, forecasts the dry straight record result that coincide.See also shown in the table 1, be certain wind energy turbine set prediction error statistics, its forecast precision height, level at home is in a leading position.

The root-mean-square error statistical form of certain wind energy turbine set forecast power of table 1

(6) interface display

User's display interface among the present invention can to inquiring about on the same day and historical data, can be realized the dynamic change demonstration of wind power and one day generating calculation of total at different wind energy turbine set.

Above content is to further describing that the present invention did in conjunction with concrete preferred implementation; can not assert that the specific embodiment of the present invention only limits to this; for the general technical staff of the technical field of the invention; without departing from the inventive concept of the premise; can also make some simple deduction or replace, all should be considered as belonging to the present invention and determine scope of patent protection by claims of being submitted to.

Claims (2)

1. a general wind power short-period forecast method is characterized in that, may further comprise the steps:

(1) gathers global large scale weather forecast field data and provide initial field and 7 days lateral boundary conditions by a definite date for the WRF pattern; Collection landform, vegetation, extra large temperature data provide underlying surface information for the WRF pattern;

(2) set up WRF mode computation meteorological field information; Described WRF pattern utilizes following equation group to carry out the calculating of wind speed, temperature, air pressure forecast fields;

The Eulerian equation of flux form:

${\∂}_{t}U+(\▿\·V_u)-{\∂}_x\left(p{\∂}_{\mathrm{\η}}\mathrm{\φ}\right)+{\∂}_{\mathrm{\η}}\left({p\∂}_x\mathrm{\φ}\right)=F_U$

${\∂}_{t}V+(\▿\·\mathrm{Vv})-{\∂}_y\left(p{\∂}_{\mathrm{\η}}\mathrm{\φ}\right)+{\∂}_{\mathrm{\η}}\left(p{\∂}_y\mathrm{\φ}\right)=F_V$

${\∂}_{t}W+(\▿\·\mathrm{Vw})-g({\∂}_{\mathrm{\η}}p-\mathrm{\μ})=F_w$

${\∂}_t\mathrm{\Θ}+(\▿\·\mathrm{V\θ})=F_{\mathrm{\Θ}}$

${\∂}_t\mathrm{\μ}+(\▿\·V)=0$

${\∂}_t\mathrm{\φ}+{\mathrm{\μ}}^{-1}[(V\·\▿\mathrm{\φ})-\mathrm{gW}]=0$

The density equation:

${\∂}_{\mathrm{\η}}\mathrm{\φ}=-\mathrm{\α\μ}$

Wherein, η=(p _h-p _Ht)/μ, μ=(p _Hs-p _Ht), p _hBe the air pressure of gas place layer, p _HsBe surface pressure, p _HtBe mode layer top air pressure; V=μ v=(U, V, W),

Θ=μ θ, (u, v w), are the component of speed air quantity in two horizontal directions and a vertical direction to v=, and θ is a megadyne temperature, and φ=gz, g are acceleration of gravity, and α=1/ ρ, ρ are atmospheric density;

(3) large scale weather forecast field data that step (1) is gathered and the WRF pattern in the underlying surface information input step (2) obtain wind speed, wind direction, temperature, the air pressure forecast data in the wind energy turbine set scope;

(4) wind speed is corrected;

By near the anemometer tower the wind energy turbine set survey wind data and the same period wind energy turbine set the power trace data utilize multiple regression procedure to set up wind speed to correct model, step (3) is obtained forecasting that wind speed is corrected obtains the forecast amendment wind speed;

(5) above-mentioned steps (4) is obtained the forecast amendment wind speed and utilize the power of fan curve to obtain each blower fan to exert oneself, adding up obtains the wind energy turbine set gross capability.

2. a kind of according to claim 1 general wind power short-period forecast method is characterized in that the equation of correcting model in the step (4) is:

y＝90.8071+0.610167×a-0.0360962×b-0.102678×c

Wherein y represents to correct the back wind speed, and unit is: m/s; A represents to forecast wind speed, and unit is: m/s; B represents to forecast temperature, and unit is: degree centigrade; C represents to forecast air pressure, and unit is: hPa.

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